The increasing availability of genome wide data sets promise to shed light into the etiology and pathophysiology of genetically complex disorders, including substance abuse and dependence. There remain significant challenges, however: although there is evidence for significant heritability, genome wide association studies have typically revealed small effect sizes, possibly due to the polygenic nature of the disorders. The brain-wide gene expression data sets from the Allen Institute offers new data sources that could be used to group genes together based on similarities in their expression profiles in anatomic space, thus enhancing the power of statistical tests in genome-wide studies. Due to the unprecedented spatial resolution in these data sets, with genome-wide and brain-wide coverage, specific hypotheses involving intercellular biochemical networks as well as brain-wide neural networks can also be examined. At the Allen Institute and at Cold Spring Harbor Laboratory, we have been collaboratively analyzing the Allen Brain Atlas (ABA) adult mouse brain data set, and preliminary results demonstrate that the spatial co-expression patterns of genes are indeed a rich source of information. In this application, we intend to focus this analysis on addiction-related gene sets, in consultation with experts on addiction research and integrating relevant online information resources. Specific aims in the first year (R21 phase) include (1) development and refinement of software and web-based tools for analysis of co-expression patterns in gene sets and (2) multivariate analysis of an initial set of addiction related genes. The first year will focus on the adult mouse brain data set that is already at hand. In subsequent years (years 2-4, R33 phase), we will extend the co-expression analysis to mouse developmental and spinal cord data sets (Aim 1), and human brain data sets (Aim 2), that are scheduled to become available during this period. Additionally, we will mine existing databases and the literature to augment our initial gene lists as well as to develop a database of associations between substance abuse phenotypes and corresponding brain areas (Aim 3). This will allow us to more fully analyze the intra and intercellular networks that may be involved in addiction. Finally, we will make the computational tools and analysis results developed as part of our research publicly available in the form of a web portal (aim 4). PUBLIC HEALTH RELEVANCE: The identification of genes and gene networks driving drug abuse and addiction is a major current challenge in addiction genetics. The public presentation of tools for understanding spatially mapped genomic datasets such as the ABA will have major impact on researchers aiming to understand these genetic networks and pathways. The proposed work will encompass both the identification of key addiction gene clusters as well as the generation of useful online methods for addiction researchers.